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High Performance computing Reinforcement Learning Framework for Power System Control

High Performance Computing Reinforcement Learning Framework ...

引用

Innovative Smart Grid Technologies (ISGT)

作者： Ivana Damjanović Ivica Pavić Mario Brčić Roko Jerčić Dept. of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Dept. of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Secondary Systems Dept. Croatian Transmission System Operator Zagreb Croatia

In this paper, the integration of a power system simulator and reinforcement learning (RL) tools and frameworks is presented. A proposed framework is easily applicable and can serve as a framework for further developing, training, and benchmarking RL algorithms on more complex tasks of power system control. The usage of standard RL frameworks enables a broad range of state-of-the-art algorithms to be implemented with high performance, scalability, and substantial code reuse. Also, the proposed framework design is suitable for scaling onto high-performance computing (HPC) clusters which significantly speeds up the computation. The IEEE 14-bus system is selected to show the simulation results of the proposed method.

关键词： Training High performance computing Scalability Power system control Clustering algorithms Reinforcement learning Computer architecture

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QUBO Coefficient Dynamic Ratio Shrinking Method for Quantum Annealers

QUBO Coefficient Dynamic Ratio Shrinking Method for Quantum ...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Yuta Yachi Masashi Tawada Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University Green Computing Systems Research Organization Waseda University

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum annealers search for optimal solutions to a combinatorial optimization problem by solving a quadratic unconstrained binary optimization (QUBO) model. Due to integrated control errors (ICEs), input QUBO coefficients are temporarily erroneous while a quantum annealer is running. Previous studies indicate that ICEs prevent quantum annealer from accurately running to obtain an optimal solution. This paper proposes a new method for reducing the ICE-induced noise effect. After a QUBO is input to a quantum annealer, the range of the QUBO coefficients is scaled within the hardware limitation. At that time, if the range is too large, the lowest absolute value (LAV) and near-LAV coefficients of the QUBO become too much small after scaling and these small coefficients can be more sensitive to the noise. To reduce the noise effect, our proposed method shrinks the range of QUBO coefficients by splitting it into multiple QUBOs. In splitting, LAV and near-LAV coefficients are left only in one QUBO, and the other coefficients are equally split so that each QUBO has a shrunk range. Finally, comparing the (quasi- )optimal solutions obtained from all split QUBOs, we obtain a solution closer to the original optimal solution. Experimental evaluation results show that the proposed method obtains more near-optimal solutions than the QUBO input as-is for all benchmarks.

关键词： Annealing Noise Benchmark testing Quantum annealing Search problems Ice Hardware Optimization Centralized control

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Rohit Dahatonde, Shubham Acharjya, Srimanta Kumar Verma, Sourabh Singh Dhaka, Vijaypal Singh Rawat, Ramesh Singh School Of Computing & Intelligent Systems Manipal University Jaipur India C. V Raman Global University Department of Computer Science and engineering Bhubaneswar India Graphic Era deemed to be University Dept. of computer Science and Engg Uttrakhand Deharadun India

ISBN: (纸本)9798350379716

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency. © 2024 IEEE.

关键词： Satellite imagery

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Non-zero Coefficients Removing Method to Improve the Ising Machine Solving Performance

Non-zero Coefficients Removing Method to Improve the Ising M...

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Quantum computing and engineering (QCE), IEEE International Conference on

作者： Kinya Iwata Masashi Tawada Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University Green Computing Systems Research Organization Waseda University

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The performance of an Ising machine in solving combinatorial optimization problems is significantly influenced by the energy landscape of an input quadratic unconstrained binary optimization (QUBO). Previous studies have shown that the number of non-zero terms in QUBO can control the energy landscape. In this paper, we propose a QUBO conversion method, which efficiently removes non-zero coefficient terms in QUBO to improve the solving performance of Ising machines. Our pro-posed method uses the removal ratio parameter, which defines the amount of removed non-zero terms, and efficiently removes the non-zero coefficient terms in QUBO. We confirm the effectiveness of the proposed method compared to the conventional method using the capacitated vehicle routing problem (CVRP) on an Ising machine. Experimental results demonstrate that our proposed method improves the path length in CVRP up to 17% compared to the conventional method.

关键词： Quantum computing Vehicle routing Optimization

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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Harnessing the Power of Artificial Intelligence in Materials Science: An Overview

Harnessing the Power of Artificial Intelligence in Materials...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adedotun Adetunla Esther Akinlabi Tien Chien Jen Samuel-Soma Ajibade Dept. of Mechanical Engineering Science University of Johannesburg South Africa Dept. of Mechanical & Construction Engineering Northumbria University Newcastle United Kingdom Dept. of Computing & Information Systems School of Engineering & Technology Sunway University Malaysia

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The integration of artificial intelligence (AI) into the realm of material science has ushered in a new era, by changing the process of material discovery and design. Leveraging advanced computational methods, machine learning algorithms, and predictive modeling, AI accelerates the identification of novel materials with tailored properties. From quantum simulations to high-throughput experimentation, AI-driven techniques enable rapid screening and prediction of material behaviors, significantly reducing the time and resources traditionally required for innovation. This synergy has paved the way for the creation of smart and adaptive materials, responsive to external stimuli and tailored for specific applications across industries. The marriage of AI and material science extends beyond discovery, encompassing efficient process optimization, manufacturing improvements, and the management of vast datasets through materials informatics. Challenges, including ethical considerations, data privacy, and responsible AI practices, must be navigated for sustainable integration. Looking forward, the collaborative potential of AI and material science promises continuous advancements. Ongoing research in machine learning, deep learning, and materials informatics anticipates breakthroughs in material applications, pushing the boundaries of innovation and sustainability. The union of AI and material science not only reshapes the landscape of scientific discovery but also holds the key to unlocking unprecedented opportunities for intelligent materials that will define the technological future.

关键词： Materials science and technology Technological innovation Ethics Collaboration Medical services Predictive models Artificial intelligence

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Analysing the Roles of Robotics in Manufacturing Organizations in the Era of Industry 4.0

Analysing the Roles of Robotics in Manufacturing Organizatio...

引用

Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The era of Industry 4.0 has ushered in a new approach in manufacturing, leveraging advanced technologies to transform traditional production methods. Robotics is the science of using robots to improve manufacturing processes with automation, flexibility, and efficiency. This article aims to comprehensively examine the role of robotics in manufacturing within the context of Industry 4.0. It seeks to explore the fundamental principles underlying robotics, their integration with Industry 4.0 technologies, applications across diverse manufacturing sectors, challenges faced in implementation, and the future directions of robotics in shaping the manufacturing landscape.

关键词： Industries Ethics Technological innovation Service robots Collaboration Transforms Market research

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BiBE: A Self-supervised Contrastive Learning Architecture for Malware Detection

BiBE: A Self-supervised Contrastive Learning Architecture fo...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Yu Wang Mingdi Xu Kexin Luo Hui Tong Chaoyang Jin Bo Xie Dept. of Computing Environment Wuhan Digital Engineering Institute Wuhan China Dept. of Systems Engineering Wuhan Digital Engineering Institute Wuhan China

Malware detection has become significantly more difficult due to the constant evolution of malware and the emergence of various variants. While machine learning-based malware detection method solves the limitations of traditional methods to a certain extent, the vast majority of current work uses supervised learning methods, which require large sets of labeled data, which means a huge amount of manpower and time cost. Self-supervised learning can solve these problems by extracting information from unlabeled data for learning. In this paper, we introduce BiBE, a malware detection architecture based on self-supervised learning framework. We first preprocess the samples with normalization, complete the missing information of features, and perform vectorization processing. Then, after learning and encoding the sample features by setting proxy tasks, we use comparative learning to distinguish similar samples, achieve the division of positive and negative samples and assign pseudo-labels. Finally, the labeled samples and pseudo-labeled samples are put into the Bi-LSTM network to train the classifier to detect malware. Moreover, we also evaluate the model on a public dataset. The evaluation shows that the BiBE model has good convergence and generalization in training, achieves excellent detection performance, and outperforms other existing methods, verifying the feasibility of the BiBE model.

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Saliency-based Explainable Artificial Intelligence (XAI) for Interpreting U-Net Semantic Segmentation of Synthetic Liquid Spray Images

Saliency-based Explainable Artificial Intelligence (XAI) for...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Chin, Teck Wai Lau, Sian Lun Wong, Richard T.K. Bravo, Luis Koneru, Rahul Babu Khare, Prashant Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Malaysia Dept. of Engineering School of Engineering and Technology Sunway University Bandar Sunway Malaysia DEVCOM Army Research Laboratory Army Research Directorate Aberdeen Proving GroundMD21005 United States Department of Aerospace Engineering University of Cincinnati CincinnatiOH45221-0070 United States Department of Aerospace Engineering University of Maryland College ParkMD20742 United States

ISBN: (纸本)9798331530303

Deep learning models, particularly U-Net, have been widely used for image segmentation tasks in various domains such as medical, biological, autonomous driving, and industrial applications. However, the black-box nature of these models raises public concerns about the lack of transparency and interpretability of their decision-making processes. Unlike typical static objects, the segmentation of dynamic objects from synthetic fluid datasets are more complicated as the properties like position and shape change over time. This paper investigates these issues by exploring the uses of saliency-based Explainable Artificial Intelligence (XAI) techniques such as Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++ and Segmentation Grad-CAM (Seg-Grad-CAM) to generate heatmap visualizations for providing insights into the prediction of U-net semantic segmentation. Using a dataset of synthetic liquid spray images, we systematically apply these techniques to visualize the model’s attention and highlights the properties and regions of the image that are influential in the model’s predictions for different classes such as lobe, ligaments and droplets. Our results demonstrate that while all techniques provide interpretable visualizations, Seg-Grad-CAM and Grad-CAM++ offer better localization of salient features, providing more precise explanations for U-Net’s segmentation decisions. The comparison of the results using quantitative analysis such as Pointing Game Accuracy and Mean Localization Error also indicated that Seg-Grad-CAM and Grad-CAM++ are more effective for interpreting complex segmentation models in liquid spray analysis. ©2024 IEEE.

关键词： Semantic Segmentation

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Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study

Synthetic Time Series Data Generation for Healthcare Applica...

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Annual Conference on Information Sciences and systems (CISS)

作者： Ainaz Jamshidi Muhammad Arif Sabir Ali Kalhoro Alexander Gelbukh Dept. of Information Systems University of Maryland Baltimore County USA Dept. of Information Systems Colorado State University USA Dept. of Electrical Engineering North Dakota State University USA Dept. of Computing Research Center Instituto Politécnico Nacional Mexico

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

The generation of high-quality medical time series data is essential for advancing healthcare diagnostics and safeguarding patient privacy. Specifically, synthesizing realistic phonocardiogram (PCG) signals offers significant potential as a cost-effective and efficient tool for cardiac disease pre-screening. Despite its potential, the synthesis of PCG signals for this specific application received limited attention in research. In this study, we employ and compare three state-of-the-art generative models from different categories — WaveNet, DoppelGANger, and DiffWave — to generate high-quality PCG data. We use data from the George B. Moody PhysioNet Challenge 2022. Our methods are evaluated using various metrics widely used in the previous literature in the domain of time series data generation, such as mean absolute error and maximum mean discrepancy. Our results demonstrate that the generated PCG data closely resembles the original datasets, indicating the effectiveness of our generative models in producing realistic synthetic PCG data. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs, in order to address the current scarcity of abnormal data. We hope to improve the robustness and accuracy of diagnostic tools in cardiology, enhancing their effectiveness in detecting heart murmurs.

关键词： Heart Measurement Time series analysis Pipelines Medical services Data collection Data models Robustness Forecasting Phonocardiography

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